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Post-Conversation Analysis allows your agent to automatically analyze conversations after they end and extract useful insights. These insights convert conversations into structured data that can be stored, analyzed, or sent to other systems. You can define multiple insight fields and instruct the AI to extract specific information from each conversation.

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Enable Post-Conversation Analysis

To enable and configure Post-Conversation Analysis:
  1. Open your Agent Builder.
  2. From the side panel, click Agent Settings.
  3. Expand the Post-Conversation Analysis section.
  4. Toggle the switch ON to enable the analysis
PCA
Once enabled, the selected model (for example GPT-4o mini) will analyze conversations after they end and extract the configured insights.

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Add a New Insight Field

To extract specific information from conversations, you can create custom insight fields.
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  1. In the Post-Conversation Analysis section, click Add New Field.
  2. Select the type of insight you want to create.
  3. Enter an Insight Name.
  4. Provide Analysis Instructions that tell the AI what to extract from the conversation.
  5. Click Submit.
The AI will analyze each conversation and populate the field based on your instructions.

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Insight Types

SigmaMind supports four types of insight fields.

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Text

The Text field is used when you want the AI to generate written output. Common use cases include:
  • Conversation summaries
  • Main reason for the call
  • Key discussion points
Example Insight Name
conversation summary Analysis Instructions 
Write a concise summary of the conversation including the main topic and final outcome.

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Selector

The Selector field allows the AI to choose one value from predefined options. This is useful for categorizing conversations into structured outcomes. Example Insight Name
call outcome Analysis Instructions 
Select the final outcome of the conversation from the predefined options.
Options might include:
  • Billing issue
  • Technical issue
  • General inquiry
  • Sales inquiry

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Boolean

The Boolean field returns a true or false value. This is useful for determining whether a specific event occurred during the conversation. Example Insight Name
appointment booked Analysis Instructions 
Return true if an appointment was booked during the conversation, otherwise return false.

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Number

The Number field allows the AI to return a numeric value. This is useful for scoring or counting information from the conversation. Example Insight Name
sentiment score Analysis Instructions 
Rate the overall customer sentiment from 1 to 10 where 1 is very negative and 10 is very positive.

Managing Insights After creating an insight field, you can:
  • Edit the field to update the instructions
  • Delete the field if it is no longer required
  • Add multiple fields to capture different insights from conversations
You can also choose which model should be used for the analysis.

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Sending Extracted Data

The insights extracted through Post-Conversation Analysis can be sent to external systems using SigmaMind Webhooks. Using webhooks, you can automatically send the extracted data to:
  • CRM systems
  • Google Sheets
  • Databases
  • Automation platforms such as n8n or Zapier
  • Custom applications
This helps automate workflows and store conversation insights for further analysis.
Post-Conversation Analysis is billed separately based on the chat message pricing of the selected LLM model used for the analysis

Post Conversation Analysis